Samuel Wamba Fosso

Last updated
Samuel Wamba Fosso
Nationality Cameroonian
Occupation(s)Researcher, author, and academic
Academic background
EducationBSc in Mathematics
MSc in Mathematics
MS in E-commerce
PhD in Industrial Engineering
Habilitation in Information Systems
Alma mater University of Yaoundé I
Sherbrooke University
HEC Montréal
Montreal University
Télécom Paris
Thesis An empirical study of the impact of RFID technology and the EPC Network on supply chain management: The case of the retail industry (2009)

Samuel Wamba Fosso is a Cameroonian researcher, author, and academic. He is a professor at TBS Education in France and a Distinguished Visiting professor at The University of Johannesburg, South Africa. He was a visiting professor of Artificial Intelligence at Bradford University from September 2020 to September 2021 [1]

Contents

Fosso's research focuses on various aspects of artificial intelligence in business, including business analytics, big data, social media, and open data. Additionally, he has explored the business value of information technology, inter-organizational system adoption and its impacts, supply chain management, electronic commerce, and blockchain. [2] He has authored 5 books, including Enterprise and Organizational Modeling and Simulation, and Transformation de la supply chain grâce aux systèmes d’information : Apport de l’Internet des Objets and has written book chapters and journal articles.

Fosso is an academic founder of RFID Academia. He is most known for his contributions to big data analytics and enterprise, which is attributed to the high number of published articles and citations. He ranks among the 1% most cited scholars in the world for the years 2020, 2021, and 2022 based on Clarivate Analytics’ Highly Cited Researchers List. [3]

Education

Fosso studied for a bachelor's degree in mathematics at the University of Yaoundé I, which he completed in 1996. Later, in 2000, he received his first master's degree in mathematics from Sherbrooke University in Canada, followed by a second master's degree in E-commerce from Montreal University in Canada. For his Ph.D., he researched Industrial Engineering at Montreal University, specializing in the impact of RFID technology and the EPC Network on supply chain management in the retail industry. His thesis was titled "An empirical study of the Impact of RFID Technology and the EPC Network on supply chain management: The Case of the retail industry". Additionally, he earned his Habilitation to Conduct Research in management science, information systems at the Telecom Business School, University of Évry at Essonne, France in 2015, with a thesis on the "Transformation of the Supply Chain Management through Inter-Organizational Information Systems: The Contribution of the Internet of Things". [1]

Career

In 2006, Fosso held a brief appointment as a part-time professor in the School of Business at Ottawa University before becoming a professor at Academia RFID in Montreal. Afterwards, he served as an assistant professor of information technology at the University of Wollongong in Australia. Between 2012 and 2016, he held the position of Professor of Information Systems and Supply Chain Management at NEOMA Business School. He is a professor at Toulouse business school Education in France, a distinguished visiting professor at the University of Johannesburg in South Africa, and a visiting professor of artificial intelligence at Bradford University. He is also a visiting professor at the Catholic University of Central Africa. [2]

Research

Fosso's research interests encompass a broad spectrum of topics, with a primary focus on Big Data and Business Analytics, Radio frequency identification (RFID), Electronic Commerce, Mobile Business, ERP, Supply Chain Management, Management of Innovation, Digital Transformation, and Artificial Intelligence. Additionally, he has secondary research interests in areas such as e-government, Green Computing, Geographic Information Systems, IT-enabled Disasters Management, and IT-enabled Social Inclusion. [4] His work has been published in recognized journals and has been cited widely throughout his career. [5] [3]

One of Fosso's primary research areas is supply chain management, in which he has explored the potential use of various technologies, such as Radio Frequency Identification (RFID), [6] Artificial Intelligence (AI), [7] and Blockchain, [8] o improve supply chain processes and performance. He highlighted the role of technology in enhancing supply chain operations and ensuring companies' competitiveness. [9] Furthermore, he examined the potential impact of AR in supply chain and logistics management in 2021 [10] and discussed different technologies' involvement and potential impact on managing various business models in his book, Managing the Digital Transformation: Aligning Technologies, Business Models, and Operations. His book also described the fundamental aspects of integrating technology, organizational structures, operations, and supply chain management to facilitate a successful digital transformation.

Big data analytics (BDA)

Fosso has conducted extensive research in the area of big data analytics and has explored its characteristic, challenges, definitional aspects, and types in different business modules. [11] In a combined study, he identified the importance of system and information quality in big data analytics as the key component to enhance business [12] and also proposed a big data analytics capability (BDAC) model to improve the firm performance (FPER). [13] [14] While analyzing big data and predictive analytics (BDPA) for supply chain and organizational performance, he indicated that connectivity, information sharing, and top management commitment positively influence BDPA acceptance, which in turn leads to BDPA assimilation through routinization, and ultimately positively impacts SCP and OP. [15] [16] In 2019, he published a paper that presented the positive influence of BDPA on social and environmental sustainability, [16] followed by a related study mentioning the effects of BDA on performance and supply chain agility and accountability, and its implementation at organizational level. [17] Furthermore, in 2020 he developed a consumer goods company innovation (CGCI) conceptual framework, showcasing how digital BDA firms aid consumer goods companies in product testing and innovation prior to market launch. [18]

Radio frequency identification (RFID)

Fosso has been accredited for his research in the field of radio frequency identification, he has investigated its potential [6] and implementation in several business models, particularly in electronic commerce. [19] In 2006, he indicated RFID as a disruptive technology as it requires major redesigning, supports new business modules and promotes higher electronic integration between supply chain members. [20] Later in the same year, he demonstrated the integration of RFID technology into information systems applications, highlighting how it leads to process optimization, [21] and tracked the key performance indicators that aid to evaluate its impact RFID technology on a five-layer supply chain in the utility sector [22] and in ecommerce applications. [23] Afterwards, in 2008, he explored the impact of RFID and electronic product code (EPC) on mobile B2B eCommerce and identified it as an appropriate approach that helps in fostering higher level of information and improves “shipping,” “receiving,” and “put-away” processes. [24] In a collaborative study with Harold Beck in 2008, he elucidated that buyer-supplier relationship plays a crucial role in shaping the RFID infrastructure and the consequences related to its implementation, [25] and also mentioned the potential of RFID and EPC in enhancing the information flow in retail supply chain. [26] In 2012, he explained how RFID act like an enabler to facilitate the seamless integration of timely and accurate data flows into information systems, streamline business processes through automation, enhance system-to-system communication, and improve inter- and intra-organizational integration of business processes. [27]

Artificial intelligence (AI)

Fosso has also focused his research expertise in the field of artificial intelligence with a particular interest in comprehending its implementation and practices [28] and its impact on enhancing business values and firm performance [29] and organizational and process levels. [30] In a collaborative study he proposed a Multi-criteria decision-making (MCDM) technique that uses AI algorithms like Fuzzy systems, Wavelet Neural Networks (WNN), and Evaluation based on Distance from Average Solution (EDAS) to discover patterns in AI techniques for creating SCRes strategies. [7] In 2022, he published a paper on the implementation of AI in ecommerce and provided guidelines to use information research for it, [31] and have discussed the positive influence of AI in operational supply chain management [32] and organizational and customer agility. [33] Furthermore, he proposed a method, known as informed AI (IAI) that involves the incorporation of human domain expertise into AI, resulting in more effective and dependable procedures for data labeling and model explainability. [34] Apart from business modules, he has also worked on the implementation of AI in digital health [35] as well as its Implications for information systems research. [36]

Block chain technologies

Fosso has also examined various aspects of blockchain technologies, including their implementation in supply chain management, associated challenges, [8] and factors enabling their use in logistics. [37] His research on blockchain adoption by supply chain management and operations has revealed that it is influenced by effort expectancy, social influence, facilitating influence, and trust. [38] [39] Additionally, he explored the potential influence of block chain technologies on supply chain management and indicated an improved supply chain performance. [40] In related research, he clarified the definitions of Bitcoin, Blockchain, and Fintech and presented their applications, benefits, and challenges in various industries. [41]

Bibliography

Selected books

Selected articles

Related Research Articles

<span class="mw-page-title-main">Supply chain management</span> Management of the flow of goods and services

In commerce, supply chain management (SCM) deals with a system of procurement, operations management, logistics and marketing channels, through which raw materials can be developed into finished products and delivered to their end customers. A more narrow definition of supply chain management is the "design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronising supply with demand and measuring performance globally". This can include the movement and storage of raw materials, work-in-process inventory, finished goods, and end to end order fulfilment from the point of origin to the point of consumption. Interconnected, interrelated or interlinked networks, channels and node businesses combine in the provision of products and services required by end customers in a supply chain.

Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

Traceability is the capability to trace something. In some cases, it is interpreted as the ability to verify the history, location, or application of an item by means of documented recorded identification.

<span class="mw-page-title-main">SAS (software)</span> Statistical software

SAS is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics. SAS' analytical software is built upon artificial intelligence and utilizes machine learning, deep learning and generative AI to manage and model data. The software is widely used in industries such as finance, insurance, health care and education.

<span class="mw-page-title-main">Business analyst</span> Person who analyses and documents business processes

A business analyst (BA) is a person who processes, interprets and documents business processes, products, services and software through analysis of data.The role of a business analyst is to ensure business efficiency increases through their knowledge of both IT and business function.

Real-time business intelligence (RTBI) is a concept describing the process of delivering business intelligence (BI) or information about business operations as they occur. Real time means near to zero latency and access to information whenever it is required.

Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription.

<span class="mw-page-title-main">Auction software</span> Application software technology for auctions

Auction software is application software, that can either be deployed on a desktop, on a web server or as a smart contract on a blockchain virtual machine. This software is used by auctioneers and participants of online auctions such as eBay. Smart contracts replace an auctioneer's server, if the auctioneer is not trusted.

The digital supply chain is a new media term which encompasses the process of the delivery of digital media, be it music or video, by electronic means, from the point of origin to destination (consumer). In much the same manner a physical medium must go through a “supply chain” process in order to mature into a consumable product, digital media must pass through various stages in processing to get to a point in which the consumer can enjoy the music or video on a mobile device, computer, or television set.

Digital transformation (DT) is the process of adoption and implementation of digital technology by an organization in order to create new or modify existing products, services and operations by the means of translating business processes into a digital format.

<span class="mw-page-title-main">Marine technology</span> Technologies used in marine environments

Marine technology is defined by WEGEMT as "technologies for the safe use, exploitation, protection of, and intervention in, the marine environment." In this regard, according to WEGEMT, the technologies involved in marine technology are the following: naval architecture, marine engineering, ship design, ship building and ship operations; oil and gas exploration, exploitation, and production; hydrodynamics, navigation, sea surface and sub-surface support, underwater technology and engineering; marine resources ; transport logistics and economics; inland, coastal, short sea and deep sea shipping; protection of the marine environment; leisure and safety.

A blockchain is a distributed ledger with growing lists of records (blocks) that are securely linked together via cryptographic hashes. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. Since each block contains information about the previous block, they effectively form a chain, with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks.

<span class="mw-page-title-main">Smart manufacturing</span> Broad category of manufacturing

Smart manufacturing is a broad category of manufacturing that employs computer-integrated manufacturing, high levels of adaptability and rapid design changes, digital information technology, and more flexible technical workforce training. Other goals sometimes include fast changes in production levels based on demand, optimization of the supply chain, efficient production and recyclability. In this concept, as smart factory has interoperable systems, multi-scale dynamic modelling and simulation, intelligent automation, strong cyber security, and networked sensors.

Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.

The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.

Whole-of-Government Approach (“WGA”) refers to the joint activities performed by diverse ministries, public administrations and public agencies in order to provide a common solution to particular problems or issues, and involve some form of cross-boundary work and restructuring.

Digital agriculture, sometimes known as smart farming or e-agriculture, is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution. Other definitions, such as those from the United Nations Project Breakthrough, Cornell University, and Purdue University, also emphasize the role of digital technology in the optimization of food systems.

<span class="mw-page-title-main">Nada Sanders</span> American university professor

Nada R. Sanders is an American university professor specializing in forecasting and supply-chain management. She is the Distinguished Professor of Supply Chain Management at the D’Amore-McKim School of Business at Northeastern University. She is also a research scholar, academic editor, reference book author, keynote speaker, business consultant, and corporate board member. Her forecasts describing the impact of the economic crisis on supply disruptions resulting from the COVID-19 pandemic received media coverage. Her latest book The Humachine, as well as her 2023 article published in HBR explore the influence of artificial intelligence over world business, culture and skills.

<span class="mw-page-title-main">Justin Goldston</span> American academic

Justin Lee Goldston is an American academic at Penn State University. He assisted in the development of the Master's program in Supply Chain Management at Georgetown University.

James R. Langabeer II is an American decision scientist, academic, and entrepreneur known for his theories on strategy and decision-making. He is the Robert H. Graham Professor of Entrepreneurial Biomedical Informatics and Bioengineering at The University of Texas Health Science Center at Houston, and maintains faculty appointments in the department of Emergency Medicine and Psychiatry.

References

  1. 1 2 "FOSSO WAMBA Samuel – TBS Education". TBS Education.
  2. 1 2 "Samuel Fosso Wamba".
  3. 1 2 "Web of Science". www.webofscience.com.
  4. ↑ "Prof Samuel Fosso Wamba".
  5. ↑ "Fosso Wamba Samuel". scholar.google.fr.
  6. 1 2 Wamba, Samuel Fosso; Lefebvre, Louis A.; Lefebvre, Elisabeth (2006). "Enabling intelligent B-to-B eCommerce supply chain management using RFID and the EPC network: a case study in the retail industry": 281. doi:10.1145/1151454.1151502. S2CID   10397390.{{cite journal}}: Cite journal requires |journal= (help)[ non-primary source needed ]
  7. 1 2 Belhadi, Amine; Kamble, Sachin; Fosso Wamba, Samuel; Queiroz, Maciel M. (18 July 2022). "Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework". International Journal of Production Research. 60 (14): 4487–4507. doi:10.1080/00207543.2021.1950935. S2CID   237853553.
  8. 1 2 Wamba, Samuel Fosso; Queiroz, Maciel M. (June 2020). "Blockchain in the operations and supply chain management: Benefits, challenges and future research opportunities". International Journal of Information Management. 52: 102064. doi:10.1016/j.ijinfomgt.2019.102064. S2CID   212737953.[ non-primary source needed ]
  9. ↑ Fosso Wamba, Samuel; Akter, Shahriar; Coltman, Tim; W.T. Ngai, Eric (10 September 2015). "Guest editorial: information technology-enabled supply chain management". Production Planning & Control. 26 (12): 933–944. doi:10.1080/09537287.2014.1002025. S2CID   53508019.
  10. ↑ Rejeb, Abderahman; Keogh, John G.; Wamba, Samuel Fosso; Treiblmaier, Horst (October 2021). "The potentials of augmented reality in supply chain management: a state-of-the-art review". Management Review Quarterly. 71 (4): 819–856. doi:10.1007/s11301-020-00201-w.
  11. ↑ Akter, Shahriar; Wamba, Samuel Fosso (May 2016). "Big data analytics in E-commerce: a systematic review and agenda for future research". Electronic Markets. 26 (2): 173–194. doi: 10.1007/s12525-016-0219-0 . S2CID   255578555.[ non-primary source needed ]
  12. ↑ Ji-fan Ren, Steven; Fosso Wamba, Samuel; Akter, Shahriar; Dubey, Rameshwar; Childe, Stephen J. (2 September 2017). "Modelling quality dynamics, business value and firm performance in a big data analytics environment". International Journal of Production Research. 55 (17): 5011–5026. doi:10.1080/00207543.2016.1154209. hdl: 10026.1/4390 . S2CID   13782471.
  13. ↑ Akter, Shahriar; Wamba, Samuel Fosso; Gunasekaran, Angappa; Dubey, Rameshwar; Childe, Stephen J. (December 2016). "How to improve firm performance using big data analytics capability and business strategy alignment?". International Journal of Production Economics. 182: 113–131. doi:10.1016/j.ijpe.2016.08.018. hdl: 10026.1/5545 .[ non-primary source needed ]
  14. ↑ Wamba, Samuel Fosso; Gunasekaran, Angappa; Akter, Shahriar; Ren, Steven Ji-fan; Dubey, Rameshwar; Childe, Stephen J. (January 2017). "Big data analytics and firm performance: Effects of dynamic capabilities". Journal of Business Research. 70: 356–365. doi:10.1016/j.jbusres.2016.08.009. hdl: 10026.1/5544 .[ non-primary source needed ]
  15. ↑ Gunasekaran, Angappa; Papadopoulos, Thanos; Dubey, Rameshwar; Wamba, Samuel Fosso; Childe, Stephen J.; Hazen, Benjamin; Akter, Shahriar (January 2017). "Big data and predictive analytics for supply chain and organizational performance" (PDF). Journal of Business Research. 70: 308–317. doi:10.1016/j.jbusres.2016.08.004. hdl: 10026.1/6680 .[ non-primary source needed ]
  16. 1 2 Fosso Wamba, Samuel; Gunasekaran, Angappa; Dubey, Rameshwar; Ngai, Eric W. T. (November 2018). "Big data analytics in operations and supply chain management". Annals of Operations Research. 270 (1–2): 1–4. doi:10.1007/s10479-018-3024-7. S2CID   254228692.
  17. ↑ Wamba, Samuel Fosso; Dubey, Rameshwar; Gunasekaran, Angappa; Akter, Shahriar (April 2020). "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism". International Journal of Production Economics. 222: 107498. doi:10.1016/j.ijpe.2019.09.019. S2CID   211403380.[ non-primary source needed ]
  18. ↑ Mariani, Marcello M.; Fosso Wamba, Samuel (December 2020). "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies" (PDF). Journal of Business Research. 121: 338–352. doi:10.1016/j.jbusres.2020.09.012. hdl: 11585/803191 . S2CID   225024918.
  19. ↑ Castro, Linda; Wamba, Samuel Fosso (15 March 2007). "An Inside Look at RFID Technology". Journal of Technology Management & Innovation. 2 (1): 128–141.[ non-primary source needed ]
  20. ↑ Lefebvre, L.A.; Lefebvre, E.; Bendavid, Y.; Wamba, S.F.; Boeck, H. (2006). "RFID as an Enabler of B-to-B e-Commerce and Its Impact on Business Processes: A Pilot Study of a Supply Chain in the Retail Industry". Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06). pp. 104a. doi:10.1109/HICSS.2006.423. ISBN   0-7695-2507-5.[ non-primary source needed ]
  21. ↑ Bendavid, Ygal; Wamba, Samuel Fosso; Lefebvre, Louis A. (2006). "Proof of concept of an RFID-enabled supply chain in a B2B e-commerce environment". Proceedings of the 8th international conference on Electronic commerce the new e-commerce: Innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet - ICEC '06. pp. 564–568. doi:10.1145/1151454.1151461. ISBN   1-59593-392-1.[ non-primary source needed ]
  22. ↑ Bendavid, Ygal; Lefebvre, Elisabeth; Lefebvre, Louis; Wamba, Samuel (2007). "B-to-B E-Commerce: Assessing the Impacts of RFID Technology in a Five Layer Supply Chain". 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07). pp. 143c. doi:10.1109/HICSS.2007.99.[ non-primary source needed ]
  23. ↑ Bendavid, Ygal; Lefebvre, Élisabeth; Lefebvre, Louis A.; Fosso-Wamba, Samuel (January 2009). "Key performance indicators for the evaluation of RFID-enabled B-to-B e-commerce applications: the case of a five-layer supply chain". Information Systems and E-Business Management. 7 (1): 1–20. doi:10.1007/s10257-008-0092-2. S2CID   10850441.
  24. ↑ Fosso Wamba, Samuel; Lefebvre, Louis A.; Bendavid, Ygal; Lefebvre, Élisabeth (April 2008). "Exploring the impact of RFID technology and the EPC network on mobile B2B eCommerce: A case study in the retail industry". International Journal of Production Economics. 112 (2): 614–629. doi:10.1016/j.ijpe.2007.05.010.
  25. ↑ Boeck, Harold; Fosso Wamba, Samuel (9 May 2008). "RFID and buyer-seller relationships in the retail supply chain". International Journal of Retail & Distribution Management. 36 (6): 433–460. doi:10.1108/09590550810873929.
  26. ↑ Wamba, Samuel Fosso; Boeck, Harold (April 2008). "Enhancing Information Flow in a Retail Supply Chain Using RFID and the EPC Network: A Proof-of-Concept Approach". Journal of Theoretical and Applied Electronic Commerce Research. 3 (1): 92–105. doi: 10.3390/jtaer3010010 . S2CID   4302986.[ non-primary source needed ]
  27. ↑ Fosso Wamba, Samuel (3 February 2012). "Achieving supply chain integration using RFID technology: The case of emerging intelligent B-to-B e-commerce processes in a living laboratory". Business Process Management Journal. 18 (1): 58–81. doi:10.1108/14637151211215019. S2CID   1083377.
  28. ↑ Bawack, Ransome Epie; Fosso Wamba, Samuel; Carillo, Kevin Daniel André (4 February 2021). "A framework for understanding artificial intelligence research: insights from practice". Journal of Enterprise Information Management. 34 (2): 645–678. doi:10.1108/JEIM-07-2020-0284. S2CID   232163521.
  29. ↑ Wamba-Taguimdje, Serge-Lopez; Wamba, Samuel Fosso; Kamdjoug, Jean Robert Kala; Wanko, Chris Emmanuel Tchatchouang (2020). "Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities". Digital Business Transformation. Lecture Notes in Information Systems and Organisation. Vol. 38. pp. 3–18. doi:10.1007/978-3-030-47355-6_1. ISBN   978-3-030-47354-9.[ non-primary source needed ]
  30. ↑ Wamba-Taguimdje, Serge-Lopez; Fosso Wamba, Samuel; Kala Kamdjoug, Jean Robert; Tchatchouang Wanko, Chris Emmanuel (12 May 2020). "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects". Business Process Management Journal. 26 (7): 1893–1924. doi:10.1108/BPMJ-10-2019-0411. S2CID   219650014.[ non-primary source needed ]
  31. ↑ Bawack, Ransome Epie; Wamba, Samuel Fosso; Carillo, Kevin Daniel André; Akter, Shahriar (March 2022). "Artificial intelligence in E-Commerce: a bibliometric study and literature review". Electronic Markets. 32 (1): 297–338. doi:10.1007/s12525-022-00537-z. PMC   8932684 . PMID   35600916.[ non-primary source needed ]
  32. ↑ Fosso Wamba, Samuel; Queiroz, Maciel M.; Guthrie, Cameron; Braganza, Ashley (10 December 2022). "Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply chain management". Production Planning & Control. 33 (16): 1493–1497. doi:10.1080/09537287.2021.1882695. S2CID   234819649.
  33. ↑ Fosso Wamba, Samuel (December 2022). "Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility". International Journal of Information Management. 67: 102544. doi:10.1016/j.ijinfomgt.2022.102544.
  34. ↑ Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine; Fosso-Wamba, Samuel (June 2022). "Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence". International Journal of Information Management. 64: 102479. doi: 10.1016/j.ijinfomgt.2022.102479 . S2CID   246420410.
  35. ↑ Fosso Wamba, Samuel; Queiroz, Maciel M. (December 2023). "Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions". Information Systems Frontiers. 25 (6): 2123–2138. doi:10.1007/s10796-021-10142-8. PMC   8122192 . PMID   34025210.
  36. ↑ Al-Qudah, Anas Ali (2022). "Artificial Intelligence in Practice: Implications for Information Systems Research, Case Study UAE Companies". Artificial Intelligence for Sustainable Finance and Sustainable Technology. Lecture Notes in Networks and Systems. Vol. 423. pp. 225–234. doi:10.1007/978-3-030-93464-4_23. ISBN   978-3-030-93463-7.
  37. ↑ Samad, Taab Ahmad; Sharma, Rohit; Ganguly, Kunal K; Wamba, Samuel Fosso; Jain, Geetika (August 2023). "Enablers to the adoption of blockchain technology in logistics supply chains: evidence from an emerging economy". Annals of Operations Research. 327 (1): 251–291. doi:10.1007/s10479-022-04546-1. S2CID   247647614.[ non-primary source needed ]
  38. ↑ Queiroz, Maciel M.; Fosso Wamba, Samuel; De Bourmont, Marc; Telles, Renato (18 October 2021). "Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy". International Journal of Production Research. 59 (20): 6087–6103. doi: 10.1080/00207543.2020.1803511 . S2CID   225382156.
  39. ↑ Queiroz, Maciel M.; Fosso Wamba, Samuel (June 2019). "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA". International Journal of Information Management. 46: 70–82. doi:10.1016/j.ijinfomgt.2018.11.021. S2CID   106407555.
  40. ↑ Fosso Wamba, Samuel; Queiroz, Maciel M.; Trinchera, Laura (November 2020). "Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation". International Journal of Production Economics. 229: 107791. doi:10.1016/j.ijpe.2020.107791. S2CID   218945107.
  41. ↑ Fosso Wamba, Samuel; Kala Kamdjoug, Jean Robert; Epie Bawack, Ransome; Keogh, John G. (17 February 2020). "Bitcoin, Blockchain and Fintech: a systematic review and case studies in the supply chain". Production Planning & Control. 31 (2–3): 115–142. doi:10.1080/09537287.2019.1631460. S2CID   170019615.
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